Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.